Data Science
What Is Data Science ?Data science deal with all kind of structured and unstructured data, It is a field that surrounds anything related to data cleansing and analysis. Data science term used for techniques that is used for extracting insight information of data. Data Science is a technique of handling and analyzing immensely large set of data using sophisticated data analysis algorithm, one has to be properly trained to use this techniques to get a measurable result. Data Science training entitle professionals with data management technologies like big data, machine learning, python etc.
What is Python?Python is combined with dynamic typing and dynamic binding with High-level built-in data structures, it makes it very attractive for Rapid Application Development and scripting or glue language to connect existing components together. It is simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Most Programmers fall in love with Python because of its increased productivity, no compilation step, and fast edit-test- debug cycle. Debugging Python programs is also easy as a bug or bad input will not cause a segmentation fault. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and so on. Python encourages program modularity and code reuse with modules and packages.
R programming as an essential programming language :R is a very important language for many Data scientist who have to analyse the data. Many important books regarding the statistical analysis are written in R language. R is very important for Data Science as it is the first step of the data science. It is the open source of language for the platforms like LINUX, WINDOWS and MAC.
Machine learning :Machine Learning (ML) is considered a sub-set of AI. You can even say that ML is an implementation of AI. So whenever you think AI, you can think of applying ML there. As the name makes it pretty clear, ML is used in situations where we want the machine to learn from the huge amounts of data we give it, and then apply that knowledge on new pieces of data that streams into the system.
Artificial Intelligence :Artificial intelligence, or AI for short, has been around since the mid 1950s. It’s not necessarily new. But it became super popular recently because of the advancements in processing capabilities. Back in the 1900s, there just wasn’t the necessary computing power to realise AI. Today, we have some of the fastest computers the world has ever seen. And the algorithm implementations have improved so much that we can run them on commodity hardware, even your laptop or smartphone that you’re using to read this right now. And given the seemingly endless possibilities of AI, everybody wants a piece of it.
Deep Learning :Deep Learning (DL) is an advancement of ML. Even though ML is super powerful for most applications, there are situations where ML leaves a lot to be desired. That is where deep learning steps in. It is generally believed that if your training dataset is relatively small, you go with ML. But if you have huge amounts of data on which you can train a model, and if the data has too many features, and if accuracy is super important (accuracy is always important though), you take the deep learning route.
